Meta model-based global design optimization and exploration method

被引:0
|
作者
Guo, Zhen-Dong [1 ]
Song, Li-Ming [1 ]
Li, Jun [1 ]
Li, Guo-Jun [1 ]
Feng, Zhen-Ping [1 ]
机构
[1] School of Energy & Power Engineering, Xi'an Jiaotong University, Xi'an,710049, China
来源
关键词
Machine design - Evolutionary algorithms - Data mining - Optimal systems;
D O I
10.13675/j.cnki.tjjs.2015.02.007
中图分类号
学科分类号
摘要
To solve computationally expensive black box problem such as turbomachinery design optimization in an effective way, a meta-model based global design optimization and exploration method named MBOE is proposed by integrating a meta-model based global optimization algorithm named MBGO and data mining techniques. The MBGO algorithm can usually achieve the global optimum with minimum function evaluations. Data mining techniques provide a way to get insights into the interactions among parameters and uncover the mechanism behind performance improvement of the optimal design. Using MBOE, 3D design optimization and data mining of Rotor 37 blade are finished. Isentropic efficiency of the optimal design is 1.74% higher than that of the reference design. And the computing time of MBGO is just 1/5 of that by applying a modified differential evolution algorithm as the optimizer. Meanwhile, data mining results indicate that the leading edge and the 3D stacking style have great effect on the blade aerodynamic performance. The performance improvement of the optimal design is benefited from the changes of related parameters. Therefore, the correctness and effectiveness of MBOE method is demonstrated. ©, 2015, Editorial Department of Journal of Propulsion Technology. All right reserved.
引用
收藏
页码:207 / 216
相关论文
共 50 条
  • [41] A novel model-based hearing compensation design using a gradient-free optimization method
    Chen, Z
    Becker, S
    Bondy, J
    Bruce, IC
    Haykin, S
    NEURAL COMPUTATION, 2005, 17 (12) : 2648 - 2671
  • [42] Multi-levels Kriging surrogate model-based robust aerodynamics optimization design method
    Xiong, Neng
    Tao, Yang
    Lin, Jun
    Liu, Xue-Qiang
    INTERNATIONAL JOURNAL OF MODERN PHYSICS B, 2020, 34 (14-16):
  • [43] A Formal Model-Based Design Method for Robotic Systems
    Wang, Rui
    Guan, Yong
    Song, Houbing
    Li, Xinxin
    Li, Xiaojuan
    Shi, Zhiping
    Song, Xiaoyu
    IEEE SYSTEMS JOURNAL, 2019, 13 (01): : 1096 - 1107
  • [44] A Wireframe Model-Based Method for Automated Internal Design
    XU Xiaosheng
    JIN Ping
    ZHANG Lanxin
    WuhanUniversityJournalofNaturalSciences, 2016, 21 (04) : 319 - 323
  • [45] Model-Based Digital Business Ecosystems: A Method Design
    Tsai, Chen Hsi
    Zdravkovic, Jelena
    Stirna, Janis
    PERSPECTIVES IN BUSINESS INFORMATICS RESEARCH, BIR 2023, 2023, 493 : 214 - 228
  • [46] The Impact of Global Sensitivities and Design Measures in Model-Based Optimal Experimental Design
    Schenkendorf, Rene
    Xie, Xiangzhong
    Rehbein, Moritz
    Scholl, Stephan
    Krewer, Ulrike
    PROCESSES, 2018, 6 (04):
  • [47] Analytical Model-Based Design Optimization of a Transverse Flux Machine
    Hasan, Iftekhar
    Husain, Tausif
    Sozer, Yilmaz
    Husain, Iqbal
    Muljadi, Eduard
    2016 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), 2016,
  • [48] Kriging model-based multidisciplinary design optimization for turbine blade
    Han, Yong-Zhi
    Gao, Hang-Shan
    Li, Li-Zhou
    Yue, Zhu-Feng
    Hangkong Dongli Xuebao/Journal of Aerospace Power, 2007, 22 (07): : 1055 - 1059
  • [49] Towards Model-Based Optimization for Quality by Design in Biotherapeutics Production
    Ehsani, Alireza
    Kappatou, Chrysoula Dimitra
    Mhamdi, Adel
    Mitsos, Alexander
    Schuppert, Andreas
    Niedenfuehr, Sebastian
    29TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PT A, 2019, 46 : 25 - 30
  • [50] Model-based design and optimization of GSSR chromatography for peptide purification
    Santos, Tiago P. D.
    Fernandes, Rita P.
    Ribeiro, Rui P. P. L.
    Peixoto, Cristina
    Mota, Jose P. B.
    DIGITAL CHEMICAL ENGINEERING, 2023, 6